GEO DATA

Most-download articles are from the articles published in 2021 during the last three month.

Original Papers

Quantitative Study of Butterfly Diversity in Wando Quercus acuta Forest Over 5 Years (2017-2021): Sanghun Lee, Na-Hyun Ahn; GEO DATA. 2023;5(2):55-59. Published online June 20, 2023; DOI: https://doi.org/10.22761/GD.2023.0010

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Abstract PDF: This study presents the long-term quantitative data on butterflies in Wando Arboretum, which represents the only warm-temperate forest located in the southernmost part of South Korea. This arboretum has significant academic value as approximately 770 species of rare woody plants or herbs, such as the Japanese evergreen oak (Quercus acuta), found in warm temperate zones grow under natural conditions here. In this project, the butterflies in this region were studied due to their sensitivity to temperature changes. The study was conducted from March-April to October-November over 5 years (2017-2021) in the region dominated by Japanese evergreen oak. We found 1,743 individuals of 47 butterfly species belonging to five families. The acquired butterfly data could serve as a reference for the further development of a network-oriented database for assessing temporal climate changes.

Dataset on the Distribution of Ecosystem-Disturbing Plants in the Republic of Korea: Man-Seok Shin, Yu Jin Hong, Sanghun Lee; GEO DATA. 2023;5(2):66-76. Published online June 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0009

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Abstract PDF: In this study, we presented distribution data for 16 plant species designated as ecosystem-disturbing species by the Ministry of Environment of the Republic of Korea. These data include location information for the ecosystem-disturbing plants from four survey projects (Monitoring of invasive alien species designated by the wildlife protection act, Nationwide survey of non-native species in Korea, The 3rd and 4th national ecosystem survey) conducted by two agencies (National Institute of Ecology and National Institute of Environmental Research) between 2014 and 2021. Additionally, the data includes habitat environmental characteristics and administrative district information on the survey sites of the ecosystem-disturbing plants. These data have a high potential for utilization as basic information for natural environmental policies and related research by identifying the habitat characteristics of invasive alien species.

Distribution Characteristics of the Clithon retropictus in the Estuarine Wetland: Yeounsu Chu, Pyoungbeom Kim; GEO DATA. 2023;5(2):60-65. Published online June 12, 2023; DOI: https://doi.org/10.22761/GD.2023.0011

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Abstract PDF Supplementary Material: This study analyzed the distribution characteristics of Clithon retropictus (C. retropictus), an endangered species, using data from the benthic macroinvertebrate survey on estuarine ecosystems conducted in 2021-2022. A total of 5,906 individuals of C. retropictus were identified in 60 estuarine wetlands located along the eastern coast, southern coast, and Jeju area. It was confirmed to be a dominant species in certain estuarine wetlands such as Obangcheon, Gohyeoncheon, and Osucheon. The southern coast of Gyeongsangnam-do was identified as a major distribution area, indicating the need for systematic conservation and management of C. retropictus in this region. Furthermore, as a basic survey of benthic macroinvertebrates is currently being conducted in Jeolla-do, it is expected that nationwide distribution data for C. retropictus will be obtained.

Comparative Study of Machine Learning and Deep Learning Models Applied to Data Preprocessing Methods for Dam Inflow Prediction: Youngsik Jo, Kwansue Jung; GEO DATA. 2023;5(2):92-102. Published online June 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0016

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Abstract PDF: In this study, we employed representative machine learning (ML) and deep learning (DL) models previously utilized in the fields of rainfall and runoff analysis in the water resources sector. We not only performed hyperparameter tuning of the models but also considered the characteristics of the model and the combination and preprocessing (such as lag-time and moving average) of meteorological and hydrological data. We then compared and evaluated the performance of the models according to various scenarios of data characteristics and ML & DL model combinations for predicting daily water inflow. To accomplish this, we utilized meteorological and hydrological data collected from 1974 to 2021 in the Soyang River Dam Basin to examine 1) precipitation, 2) inflow, and 3) meteorological data as primary independent variables. We then employed a total of 36 scenario combinations as input data for ML & DL, applying a) lag-time, b) moving average, and c) component separation conditions for inflow. To identify the most suitable data combination characteristics and ML & DL models for predicting daily inflow, we compared and evaluated 10 different ML & DL models: 1) Linear Regression, 2) Lasso, 3) Ridge, 4) Support Vector Regression, 5) Random Forest (RF), 6) Light Gradient Boosting Model, 7) XGBoost for ML, and 8) Long Short-Term Memory (LSTM) models, 9) Temporal Convolutional Network (TCN), and 10) LSTM-TCN for DL.

Long-Term Change and Analysis of the Sedimentary Environment of Dadae Beach Using Unmanned Aerial Vehicles: Joo Bong Jeong; GEO DATA. 2023;5(2):110-117. Published online June 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0018

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Abstract PDF: Dadae Beach, located at the Nakdong river estuary, has been continuously evolving over the years, and this is the result of complex interactions between natural and artificial factors. In particular, in the case of Dadae Beach, located at the estuary of the Nakdong river estuary, it is located at the boundary between the river and the ocean, and it is an environment in which various deposition mechanisms operate. It is a very difficult research task to analyze the beach evolution mechanism, and a long-term study using precise measurement methods is required. Therefore, in this study, precision unmanned aerial surveys were conducted three times (2015, 2019, and 2021) for 5 years to identify the sedimentary characteristics of Dadae Beach, and the sedimentary environment was analyzed through the analysis of surface sediment texture characteristics. Seasonal waves and winds caused by the East Asian monsoon climate are the main mechanisms for the sedimentation of Dadae Beach, and finegrained sediments are distributed throughout the beach. In addition, the formation of sandbar, which arose rapidly due to artificial influences such as the construction of estuary banks in the past, is a major factor in the evolution of large-scale beaches. This study is meaningful in identifying the mechanism of beach evolution and presenting quantitative analysis results through comparison of precision aerial survey data over a long period of time.

The Integrated Dataset for Occurrence of Odonata Species in South Korea: Sungsoo Yoon; GEO DATA. 2022;5(2):77-86. Published online June 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0013

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Abstract PDF Supplementary Material: This study presents an integrated dataset comprising occurrence records for 102 Odonata species in South Korea. The occurrence information of the Odonata species was collected from 10 independent field survey-based datasets, spanning the period from 2014 to 2023 and provided by three agencies: National Institute of Ecology, National Institute of Environmental Research, and Global Biodiversity Information Facility (GBIF). In addition to occurrence data, the dataset includes information on the survey year, IUCN (International Union for Conservation of Nature) Red List classification, satellite-derived spectral indices and bioclimatic variables to provide valuable insights into the survey sites and habitats of Odonata species. By integrating diverse dataset, this comprehensive Odonata dataset can contribute to the validation of habitat-related traits, enhances the national checklist information, and facilitates the identification of rarity and potential habitats for Odonata species in South Korea. The integrated Odonata dataset serves as a valuable resource for ongoing Odonata research and conservation efforts in the study area.

Small Unmanned Aerial Vehicle LiDAR-based High Spatial Resolution Topographic Dataset in Russell Glacier, Greenland: Yongsik Jeong, Sungjae Lee, Seung Hee Kim, Hyun-Cheol Kim; GEO DATA. 2023;5(1):1-7. Published online March 29, 2023; DOI: https://doi.org/10.22761/GD.2023.0006

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Abstract PDF: Greenland contains a large continental glacier. The influence of glacier melting has been expanding due to global warming. Although regional monitoring based on satellite data is being conducted, the demand for local/specific variation observation has increased as rising climate temperature patterns in the polar region. In this study, a precise topographic dataset was created for Greenland’s Russell glacier using a small unmanned aerial vehicle (sUAV) onboarded LiDAR sensor. A precise digital surface model (DSM) was constructed based on LiDAR data obtained at an altitude of about 100 to 200 m, and DSM resampled to a 2 m sample distance was produced to confirm its applicability by comparing before-and-after variations. This study provides DSM data applied with a pre/post-processing used for the comparison analysis.

Review

Data used for GIS-based Flood Susceptibility Mapping: Saro Lee, Fatemeh Rezaie; GEO DATA. 2022;4(1):1-15. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.001

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Abstract PDF: The dramatic increase in flood incidents as a significant threat to human life and property, environment, and infrastructure indicates the necessity of mapping spatial distribution of flood susceptible areas to reduce destructive effects of flooding. During the last decade, the integration of the geographic information system (GIS) with the remote sensing data provide efficient means to generate a more reliable and precise flood susceptibility map. The present study contains a review of 200 articles on the application of GIS-based methods in indicating flood vulnerable areas. The papers were reviewed in terms of influential variables, study area, and the number of articles published in the last 10 years. The review shows that the number of studies has increased since 2012. The total study areas covered 39 countries that were mostly located in Asia where the major developments and infrastructures have been constructed in the floodplains. The most common study areas was Iran (44 articles, 22%), followed by India (26 articles, 13%), China (26 articles, 11%), and Vietnam (15 articles, 7.5%). More than 90 variables were considered to map flood susceptible areas that the top 5 widely used flood conditioning factor are slope (98% of total articles), followed by elevation (92% of total articles), land use/land cover (79.5% of total articles), distance to the river (76.5% of total articles), and rainfall (73% of total articles). The review implies that many natural and anthropogenic factors affect flooding and the combination of both groups of factors is necessary to accurately detect and map flood-prone parts of the study area.

Original Papers

Investigation of Cicada Density in Urban Park: Jae-Yeon Kang, Yong-Su Kwon; GEO DATA. 2023;5(2):87-91. Published online June 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0017

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Abstract PDF: In this study, we investigated the occurrence patterns and densities of cicadas in 18 urban parks in Seoul, Korea. Considering the ecological characteristics of cicadas that occured only in summer, the temporal changes of cicadas were investigated every week from June to September in 2017. Cicadas densities were measured using the final instar exuviae of cicada, which showed distinct morphological differences between species. A total of 7,369 cicada exuviae of six species were collected, and the dominant species in urban park were Hyalessa fuscata (44.8%) and Cryptotympana atrata (41.7%). There was no clear difference in the pattern of occurrence by species, but it occurred most frequently around August when the rainy season ended.

The Introduction of Naju Ground Observation Site Measurement Data and Web Service for Validation of Satellite Value-Added Products: Jong-Sung Ha, Seung-taek Jeong, Hyun-Ok Kim, Sun-Gu Lee, Dae-won Jeong, Jaeil Cho, Seo Ho Shin, Kil-Ja Kim, Dong-Kwan Kim, Jong-Min Yeom; GEO DATA. 2023;5(2):103-109. Published online June 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0012

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Abstract PDF: The Korea Aerospace Research Institute (KARI) has collaborated with Jeollanamdo Agricultural Research & Extension Services and Chonnam National University to establish a ground observation tower for evaluating the value-added products (such as surface reflectance and Normalized Difference Vegetation Index) and improving algorithms of domestic development satellites (Korea Multi-Purpose Satellite-3, 3A and 7). The ground measurement tower, installed at the Jeollanamdo Naju ground observation site (NGOS), constantly observes surface hyperspectral reflectance and atmospheric information, providing the advantage of real-time algorithm validation improvement when satellite acquires images of the site. The NGOS operates hyperspectral radiometer equipment (6 types), meteorological observation equipment (5 types), sky radiometer (1 type), and eddy flux observation equipment (2 types), along with a web service for display and data processing. The ground observation site equipment that is being installed and operated can be utilized in various fields such as carbon circle, agriculture, environment, atmosphere and climate change, in addition to validation of satellite value-added products. This study aims to introduce KARI NGOS for user data sharing and highlight the characteristics of the measured data.

Topographic Dataset around Uljin Hujeong Coast Using Multi-beam Echo Sounder and Shipborne Mobile LiDAR: Won Hyuck Kim, Chang Hwan Kim, Jong Dae Do, Won Dae Baek, Jea Ho Choi, Soon Young Choi, Byung Gil Lee, Chan Hong Park, Feel Hoon Go; GEO DATA. 2023;5(1):49-53. Published online March 30, 2023; DOI: https://doi.org/10.22761/GD.2023.0008

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Abstract PDF: We obtained a dataset through beach and submarine topography surveys around Hujeoung coast in Uljin. We conducted the beach and submarine topography surveys using small vessels from July 8 to July 11, 2016. The surveying instruments used for the surveys were Shipborne Mobile LiDAR System and Multi-beam Echo Sounder (Kongsberg EM3001). The beach topography was observed up to about 6 m from the shoreline. The width of the beach is about 30 m to 40 m. In the southeast of the survey area, there is an exposed rock with a depth of about 20 m. The area around the rock has sandy sediments. Datasets of the Hujeong coast area can be used for continuous monitoring to development of coastal erosion control system.

Sea Ice Elevation Measurements Using 3-D Laser Scanner: Minji Seo, Ji-Eun Park, Jeong-Won Park, Jinku Park, Hyun-Cheol Kim; GEO DATA. 2023;5(1):20-25. Published online March 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0004

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Abstract PDF: This study aims to introduce a sea ice elevation dataset estimated by using a 3-D laser scanner during the ice camp of the 2022 Arctic summer field survey. The equipment used is FARO’s Laser scanner FOCUS 3D X 130 HDR. The observed sea ice floe is located in the Arctic Ocean (76°13' N, 174°35') and is a multi-year ice with several melt ponds and ice ridges. We scanned eight sections separately, considering the equipment’s maximum horizontal scan range and the ice surface’s topographic features. The raw data were co-registered based on the positions of reference spheres. The result indicated a significant level of accuracy with a target-based vertical mean error of 4.8 mm. The laser scanner data were merged into point clouds ranging from 160×210 m. As a result, sea ice elevation data were generated in 0 to 2.8 m based on the minimum vertical point in the observation range. Sea ice elevation data is an essential variable in estimating the various properties of sea ice, such as ice thickness and roughness. In addition, using climatic variables such as air temperature and energy budget observed simultaneously can help to understand the physical interaction between the sea ice surface and the atmosphere on a local scale.

Articles

Construction of a Training Dataset for Vessel Distribution Prediction: The Northern Seas of Jeju Island: Yonggil Park, Taehoon Kim, Hyeon-Gyeong Han, Cholyoung Lee; GEO DATA. 2022;4(2):37-46. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.004

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Abstract PDF: Recently, interest in maritime accidents and safety-related research, such as preventing collisions between marine vessels, detecting illegal vessels, and predicting vessel routes, is increasing. Vessel location data-based vessel distribution map can support decision-making for maritime safety management, and if the vessel distribution can be predicted, it is possible to take a preemptive response for maritime security such as fishing safety management and illegal fishing prevention. In this study, a training dataset for vessel distribution prediction was constructed by collecting V-Pass data, weather warnings, and marine environment data. The result of resampling of reporting interval of vessel location data was mapped to grid data to evaluate the vessel density, and a total of 1,314,000 of training data were constructed for the study area. In the future, research to evaluate the accuracy by performing vessel distribution prediction modeling should be conducted.

AI Dataset for Road Detection using KOMPSAT Images: Hoonhee Lee, Han Oh; GEO DATA. 2022;4(1):43-48. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.005

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Abstract PDF: Information on shape and type of road present in an optical image of satellite is useful for digital mapping and monitoring of road changes. Processing and structuring optical image data collected from payloads mounted on KOMPSAT 3 and 3A can accelerate the development of road detection algorithms and the extraction of road information using them. In particular, if it is built with a learning dataset for AI (Artificial Intelligence) prepared to apply deep learning technology, the latest artificial intelligence technology in the field of computer science can be spun off to the field of satellite image-based road detection to attempt a wide range of analysis. Korea Aerospace Research Institute constructed an image dataset for AI learning using satellite optical images with Korean companies, and this paper explains the type and size of datasets along with examples of the use of the dataset. The established data can be used through the website, aihub.or.kr.

UAV Photogrammetry and LiDAR Based Dataset of Spartina anglica Distribution and High-resolution Topographic Map in Ganghwado: Keunyong Kim, Yeongjae Jang, Jingyo Lee, Joo-Hyung Ryu; GEO DATA. 2022;4(2):1-8. Published online June 30, 2022; DOI: https://doi.org/10.22761/DJ2022.4.2.001

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Abstract PDF: The Spartina anglica in the tidal flat at the southern part of Ganghwado, it is known that the distribution area has gradually expanded since it was officially announced as invasive alien species in 2015. The government and local governments are continuing their efforts to remove the S. anglica, and for this, continuous distribution change monitoring is required. This study extracted the data of distribution and extent area of S. anglica from Zenmuse P1 sensor, and generated the high-resolution Digital Elevation Model (DEM) from Zenmuse L1 sensor. Optical and Lidar images were photographed at an altitude of 70 m, and Ground Sampling Distance (GSD) of optical images was obtained at 0.9 cm and GSD of lidar images at 5 cm spatial resolution. However, the data are resampled and provided in GSD 25 cm to comply with the "National Spatial Information Security Management Regulations of the Ministry of Land, Infrastructure and Transport" and "Security Business Regulations of the National Intelligence Service".

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